OpenRaiser/Intern-Atlas huggingface dataset:
Data Aggregation Techniques for Internet of Things
Venue: arXiv cs.NI 2019
Year: 2019

The goal of this dissertation is to design efficient data aggregation frameworks for massive IoT networks in different scenarios to support the proper functioning of IoT analytics layer. This dissertation includes modern algorithmic frameworks such as non convex optimization, machine learning, stochastic matrix perturbation theory and federated filtering along with modern computing infrastructure such as fog computing and cloud computing. The development of such an ambitious design involves many open challenges, this proposal envisions three major open challenges for IoT data aggregation: first, severe resource constraints of IoT nodes due to limited power and computational ability, second, the highly uncertain (unreliable) raw IoT data is not fit for decisionmaking and third, network latency and privacy issue for critical applications. This dissertation presents three independent novel approaches for distinct scenarios to solve one or more aforementioned open challenges. The first approach focuses on energy efficient routing; discusses a clustering protocol based on device to device communication for both stationary and mobile IoT nodes. The second approach focuses on processing uncertain raw IoT data; presents an IoT data aggregation scheme to improve the quality of raw IoT data. Finally, the third approach focuses on power loss due to communication overhead and privacy issues for medical IoT devices (IoMT); describes a prediction based data aggregation framework for massive IoMT devices.
Revisiting Explicit Negation in Answer Set Programming
Venue: arXiv cs.LO 2019
Year: 2019

A common feature in Answer Set Programming is the use of a second negation, stronger than default negation and sometimes called explicit, strong or classical negation. This explicit negation is normally used in front of atoms, rather than allowing its use as a regular operator. In this paper we consider the arbitrary combination of explicit negation with nested expressions, as those defined by Lifschitz, Tang and Turner. We extend the concept of reduct for this new syntax and then prove that it can be captured by an extension of Equilibrium Logic with this second negation. We study some properties of this variant and compare to the already known combination of Equilibrium Logic with Nelson's strong negation. Under consideration for acceptance in TPLP.
Context-Aware Multipath Networks
Venue: arXiv cs.CV 2019
Year: 2019

Making a single network effectively address diverse contexts---learning the variations within a dataset or multiple datasets---is an intriguing step towards achieving generalized intelligence. Existing approaches of deepening, widening, and assembling networks are not cost effective in general. In view of this, networks which can allocate resources according to the context of the input and regulate flow of information across the network are effective. In this paper, we present Context-Aware Multipath Network (CAMNet), a multi-path neural network with data-dependant routing between parallel tensors. We show that our model performs as a generalized model capturing variations in individual datasets and multiple different datasets, both simultaneously and sequentially. CAMNet surpasses the performance of classification and pixel-labeling tasks in comparison with the equivalent single-path, multi-path, and deeper single-path networks, considering datasets individually, sequentially, and in combination. The data-dependent routing between tensors in CAMNet enables the model to control the flow of information end-to-end, deciding which resources to be common or domain-specific.
Learning Transparent Object Matting
Venue: arXiv cs.CV 2019
Year: 2019

This paper addresses the problem of image matting for transparent objects. Existing approaches often require tedious capturing procedures and long processing time, which limit their practical use. In this paper, we formulate transparent object matting as a refractive flow estimation problem, and propose a deep learning framework, called TOM-Net, for learning the refractive flow. Our framework comprises two parts, namely a multi-scale encoder-decoder network for producing a coarse prediction, and a residual network for refinement. At test time, TOM-Net takes a single image as input, and outputs a matte (consisting of an object mask, an attenuation mask and a refractive flow field) in a fast feed-forward pass. As no off-the-shelf dataset is available for transparent object matting, we create a large-scale synthetic dataset consisting of $178K$ images of transparent objects rendered in front of images sampled from the Microsoft COCO dataset. We also capture a real dataset consisting of $876$ samples using $14$ transparent objects and $60$ background images. Besides, we show that our method can be easily extended to handle the cases where a trimap or a background image is available.Promising experimental results have been achieved on both synthetic and real data, which clearly demonstrate the effectiveness of our approach.
Autoencoding with a Learning Classifier System: Initial Results
Venue: arXiv cs.NE 2019
Year: 2019

Autoencoders enable data dimensionality reduction and a key component of many (deep) learning systems. This short paper introduces a form of Holland's Learning Classifier System (LCS) to perform autoencoding building upon a previously presented form of LCS that utilises unsupervised learning for clustering. Initial results using a neural network representation suggest it is an effective approach to reduction.
The order type of scattered context-free orderings of rank one is computable
Venue: arXiv cs.FL 2019
Year: 2019

A linear ordering is called context-free if it is the lexicographic ordering of some context-free language and is called scattered if it has no dense subordering. Each scattered ordering has an associated ordinal, called its rank. It is known that the isomorphism problem of scattered context-free orderings is undecidable, if one of them has a rank at least two. In this paper we show that it is decidable whether a context-free ordering has rank at most one, and if so, its order type is effectively computable.
Mining Twitter to Assess the Determinants of Health Behavior towards Human Papillomavirus Vaccination in the United States
Venue: arXiv cs.SI 2019
Year: 2019

Objectives To test the feasibility of using Twitter data to assess determinants of consumers' health behavior towards Human papillomavirus (HPV) vaccination informed by the Integrated Behavior Model (IBM). Methods We used three Twitter datasets spanning from 2014 to 2018. We preprocessed and geocoded the tweets, and then built a rule-based model that classified each tweet into either promotional information or consumers' discussions. We applied topic modeling to discover major themes, and subsequently explored the associations between the topics learned from consumers' discussions and the responses of HPV-related questions in the Health Information National Trends Survey (HINTS). Results We collected 2,846,495 tweets and analyzed 335,681 geocoded tweets. Through topic modeling, we identified 122 high-quality topics. The most discussed consumer topic is "cervical cancer screening"; while in promotional tweets, the most popular topic is to increase awareness of "HPV causes cancer". 87 out of the 122 topics are correlated between promotional information and consumers' discussions. Guided by IBM, we examined the alignment between our Twitter findings and the results obtained from HINTS. 35 topics can be mapped to HINTS questions by keywords, 112 topics can be mapped to IBM constructs, and 45 topics have statistically significant correlations with HINTS responses in terms of geographic distributions. Conclusion Not only mining Twitter to assess consumers' health behaviors can obtain results comparable to surveys but can yield additional insights via a theory-driven approach. Limitations exist, nevertheless, these encouraging results impel us to develop innovative ways of leveraging social media in the changing health communication landscape.
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Venue: arXiv cs.CL 2019
Year: 2019

Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code.
Real-time Visual Object Tracking with Natural Language Description
Venue: arXiv cs.CV 2019
Year: 2019

In recent years, deep-learning-based visual object trackers have been studied thoroughly, but handling occlusions and/or rapid motion of the target remains challenging. In this work, we argue that conditioning on the natural language (NL) description of a target provides information for longer-term invariance, and thus helps cope with typical tracking challenges. However, deriving a formulation to combine the strengths of appearance-based tracking with the language modality is not straightforward. We propose a novel deep tracking-by-detection formulation that can take advantage of NL descriptions. Regions that are related to the given NL description are generated by a proposal network during the detection phase of the tracker. Our LSTM based tracker then predicts the update of the target from regions proposed by the NL based detection phase. In benchmarks, our method is competitive with state of the art trackers, while it outperforms all other trackers on targets with unambiguous and precise language annotations. It also beats the state-of-the-art NL tracker when initializing without a bounding box. Our method runs at over 30 fps on a single GPU.
VITAL: A Visual Interpretation on Text with Adversarial Learning for Image Labeling
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we propose a novel way to interpret text information by extracting visual feature presentation from multiple high-resolution and photo-realistic synthetic images generated by Text-to-image Generative Adversarial Network (GAN) to improve the performance of image labeling. Firstly, we design a stacked Generative Multi-Adversarial Network (GMAN), StackGMAN++, a modified version of the current state-of-the-art Text-to-image GAN, StackGAN++, to generate multiple synthetic images with various prior noises conditioned on a text. And then we extract deep visual features from the generated synthetic images to explore the underlying visual concepts for text. Finally, we combine image-level visual feature, text-level feature and visual features based on synthetic images together to predict labels for images. We conduct experiments on two benchmark datasets and the experimental results clearly demonstrate the efficacy of our proposed approach.
Monomial-Cartesian codes and their duals, with applications to LCD codes, quantum codes, and locally recoverable codes
Venue: arXiv cs.IT 2019
Year: 2019

A monomial-Cartesian code is an evaluation code defined by evaluating a set of monomials over a Cartesian product. It is a generalization of some families of codes in the literature, for instance toric codes, affine Cartesian codes and $J$-affine variety codes. In this work we use the vanishing ideal of the Cartesian product to give a description of the dual of a monomial-Cartesian code. Then we use such description of the dual to prove the existence of quantum error correcting codes and MDS quantum error correcting codes. Finally we show that the direct product of monomial-Cartesian codes is a locally recoverable code with $t$-availability if at least $t$ of the components are locally recoverable codes.
Modality Definition Synthesis for Epistemic Intuitionistic Logic via a Theorem Prover
Venue: arXiv cs.LO 2019
Year: 2019

We derive a Prolog theorem prover for an Intuitionistic Epistemic Logic by starting from the sequent calculus {\bf G4IP} that we extend with operator definitions providing an embedding in intuitionistic propositional logic ({\bf IPC}). With help of a candidate definition formula generator, we discover epistemic operators for which axioms and theorems of Artemov and Protopopescu's {\em Intuitionistic Epistemic Logic} ({\bf IEL}) hold and formulas expected to be non-theorems fail. We compare the embedding of {\bf IEL} in {\bf IPC} with a similarly discovered successful embedding of Dosen's double negation modality, judged inadequate as an epistemic operator. Finally, we discuss the failure of the {\em necessitation rule} for an otherwise successful {\bf S4} embedding and share our thoughts about the intuitions explaining these differences between epistemic and alethic modalities in the context of the Brouwer-Heyting-Kolmogorov semantics of intuitionistic reasoning and knowledge acquisition. Keywords: epistemic intuitionistic logic, propositional intuitionistic logic, Prolog-based theorem provers, automatic synthesis of logic systems, definition formula generation algorithms, embedding of modal logics into intuitionistic logic.
Uncertainty in Model-Agnostic Meta-Learning using Variational Inference
Venue: arXiv cs.LG 2019
Year: 2019

We introduce a new, rigorously-formulated Bayesian meta-learning algorithm that learns a probability distribution of model parameter prior for few-shot learning. The proposed algorithm employs a gradient-based variational inference to infer the posterior of model parameters to a new task. Our algorithm can be applied to any model architecture and can be implemented in various machine learning paradigms, including regression and classification. We show that the models trained with our proposed meta-learning algorithm are well calibrated and accurate, with state-of-the-art calibration and classification results on two few-shot classification benchmarks (Omniglot and Mini-ImageNet), and competitive results in a multi-modal task-distribution regression.
Q-MIND: Defeating Stealthy DoS Attacks in SDN with a Machine-learning based Defense Framework
Venue: arXiv cs.NI 2019
Year: 2019

Software Defined Networking (SDN) enables flexible and scalable network control and management. However, it also introduces new vulnerabilities that can be exploited by attackers. In particular, low-rate and slow or stealthy Denial-of-Service (DoS) attacks are recently attracting attention from researchers because of their detection challenges. In this paper, we propose a novel machine learning based defense framework named Q-MIND, to effectively detect and mitigate stealthy DoS attacks in SDN-based networks. We first analyze the adversary model of stealthy DoS attacks, the related vulnerabilities in SDN-based networks and the key characteristics of stealthy DoS attacks. Next, we describe and analyze an anomaly detection system that uses a Reinforcement Learning-based approach based on Q-Learning in order to maximize its detection performance. Finally, we outline the complete Q-MIND defense framework that incorporates the optimal policy derived from the Q-Learning agent to efficiently defeat stealthy DoS attacks in SDN-based networks. An extensive comparison of the Q-MIND framework and currently existing methods shows that significant improvements in attack detection and mitigation performance are obtained by Q-MIND.
DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks
Venue: arXiv cs.LG 2019
Year: 2019

The field of video compression has developed some of the most sophisticated and efficient compression algorithms known in the literature, enabling very high compressibility for little loss of information. Whilst some of these techniques are domain specific, many of their underlying principles are universal in that they can be adapted and applied for compressing different types of data. In this work we present DeepCABAC, a compression algorithm for deep neural networks that is based on one of the state-of-the-art video coding techniques. Concretely, it applies a Context-based Adaptive Binary Arithmetic Coder (CABAC) to the network's parameters, which was originally designed for the H.264/AVC video coding standard and became the state-of-the-art for lossless compression. Moreover, DeepCABAC employs a novel quantization scheme that minimizes the rate-distortion function while simultaneously taking the impact of quantization onto the accuracy of the network into account. Experimental results show that DeepCABAC consistently attains higher compression rates than previously proposed coding techniques for neural network compression. For instance, it is able to compress the VGG16 ImageNet model by x63.6 with no loss of accuracy, thus being able to represent the entire network with merely 8.7MB. The source code for encoding and decoding can be found at https://github.com/fraunhoferhhi/DeepCABAC.
SOM-Guided Evolutionary Search for Solving MinMax Multiple-TSP
Venue: arXiv cs.NE 2019
Year: 2019

Multiple-TSP, also abbreviated in the literature as mTSP, is an extension of the Traveling Salesman Problem that lies at the core of many variants of the Vehicle Routing problem of great practical importance. The current paper develops and experiments with Self Organizing Maps, Evolutionary Algorithms and Ant Colony Systems to tackle the MinMax formulation of the Single-Depot Multiple-TSP. Hybridization between the neural network approach and the two meta-heuristics shows to bring significant improvements, outperforming results reported in the literature on a set of problem instances taken from TSPLIB.
Unlocking Social Media and User Generated Content as a Data Source for Knowledge Management
Venue: arXiv cs.SI 2019
Year: 2019

The pervasiveness of Social Media and user-generated content has triggered an exponential increase in global data volumes. However, due to collection and extraction challenges, data in many feeds, embedded comments, reviews and testimonials are inaccessible as a generic data source. This paper incorporates Knowledge Management framework as a paradigm for knowledge management and data value extraction. This framework embodies solutions to unlock the potential of UGC as a rich, real-time data source for analytical applications. The contributions described in this paper are threefold. Firstly, a method for automatically navigating pagination systems to expose UGC for collection is presented. This is evaluated using browser emulation integrated with dynamic data collection. Secondly, a new method for collecting social data without any a priori knowledge of the sites is introduced. Finally, a new testbed is developed to reflect the current state of internet sites and shared publicly to encourage future research. The discussion benchmarks the new algorithm alongside existing data extraction techniques and provides evidence of the increased amount of UGC data made accessible by the new algorithm.
Learnable Parameter Similarity
Venue: arXiv cs.LG 2019
Year: 2019

Most of the existing approaches focus on specific visual tasks while ignoring the relations between them. Estimating task relation sheds light on the learning of high-order semantic concepts, e.g., transfer learning. How to reveal the underlying relations between different visual tasks remains largely unexplored. In this paper, we propose a novel \textbf{L}earnable \textbf{P}arameter \textbf{S}imilarity (\textbf{LPS}) method that learns an effective metric to measure the similarity of second-order semantics hidden in trained models. LPS is achieved by using a second-order neural network to align high-dimensional model parameters and learning second-order similarity in an end-to-end way. In addition, we create a model set called ModelSet500 as a parameter similarity learning benchmark that contains 500 trained models. Extensive experiments on ModelSet500 validate the effectiveness of the proposed method. Code will be released at \url{https://github.com/Wanggcong/learnable-parameter-similarity}.
Dilated FCN: Listening Longer to Hear Better
Venue: arXiv cs.SD 2019
Year: 2019

Deep neural network solutions have emerged as a new and powerful paradigm for speech enhancement (SE). The capabilities to capture long context and extract multi-scale patterns are crucial to design effective SE networks. Such capabilities, however, are often in conflict with the goal of maintaining compact networks to ensure good system generalization. In this paper, we explore dilation operations and apply them to fully convolutional networks (FCNs) to address this issue. Dilations equip the networks with greatly expanded receptive fields, without increasing the number of parameters. Different strategies to fuse multi-scale dilations, as well as to install the dilation modules are explored in this work. Using Noisy VCTK and AzBio sentences datasets, we demonstrate that the proposed dilation models significantly improve over the baseline FCN and outperform the state-of-the-art SE solutions.
Fairest of Them All: Establishing a Strong Baseline for Cross-Domain Person ReID
Venue: arXiv cs.CV 2019
Year: 2019

Person re-identification (ReID) remains a very difficult challenge in computer vision, and critical for large-scale video surveillance scenarios where an individual could appear in different camera views at different times. There has been recent interest in tackling this challenge using cross-domain approaches, which leverages data from source domains that are different than the target domain. Such approaches are more practical for real-world widespread deployment given that they don't require on-site training (as with unsupervised or domain transfer approaches) or on-site manual annotation and training (as with supervised approaches). In this study, we take a systematic approach to establishing a large baseline source domain and target domain for cross-domain person ReID. We accomplish this by conducting a comprehensive analysis to study the similarities between source domains proposed in literature, and studying the effects of incrementally increasing the size of the source domain. This allows us to establish a balanced source domain and target domain split that promotes variety in both source and target domains. Furthermore, using lessons learned from the state-of-the-art supervised person re-identification methods, we establish a strong baseline method for cross-domain person ReID. Experiments show that a source domain composed of two of the largest person ReID domains (SYSU and MSMT) performs well across six commonly-used target domains. Furthermore, we show that, surprisingly, two of the recent commonly-used domains (PRID and GRID) have too few query images to provide meaningful insights. As such, based on our findings, we propose the following balanced baseline for cross-domain person ReID consisting of: i) a fixed multi-source domain consisting of SYSU, MSMT, Airport and 3DPeS, and ii) a multi-target domain consisting of Market-1501, DukeMTMC-reID, CUHK03, PRID, GRID and VIPeR.
DAR-Net: Dynamic Aggregation Network for Semantic Scene Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

Traditional grid/neighbor-based static pooling has become a constraint for point cloud geometry analysis. In this paper, we propose DAR-Net, a novel network architecture that focuses on dynamic feature aggregation. The central idea of DAR-Net is generating a self-adaptive pooling skeleton that considers both scene complexity and local geometry features. Providing variable semi-local receptive fields and weights, the skeleton serves as a bridge that connect local convolutional feature extractors and a global recurrent feature integrator. Experimental results on indoor scene datasets show advantages of the proposed approach compared to state-of-the-art architectures that adopt static pooling methods.